A new approach based on OMA-empirical wavelet transforms for bearing fault diagnosis

Amplitude demodulation is a key means of diagnosing bearing faults. The quality of demodulation determines the effectiveness of spectrum analysis in detecting defects. However, the quality of the demodulated signal depends on the frequency band selected for demodulation. In this paper, a new method combining the empirical wavelet transform (EWT) and operational modal analysis (OMA) is proposed. EWT acts like a filter bank in which the support boundaries of the filter are defined using OMA. The proposed method (OMA–EWT) decomposes the signal into multiple components and kurtosis values are used to select automatically the components for performing the envelope spectrum in order to extract the frequency related to the defect. The method is validated on two test benches and a comparative study is conducted with the kurtogram. The results show that the combined OMA–EWT method can improve EWT for decomposing the signal into multiple components. Using OMA–EWT, a selection of all the components excited by the defect gives more accurate diagnostic results.     

基于最大相关峭度反褶积的轴承故障诊断方法

针对滚动轴承的故障信号是周期性冲击信号这一特性,提出了最大相关峭度反褶积(maximum correlated kurtosis deconvolution,简称MCKD)与谱峭度(spectral kurtosis,简称SK)结合的滚动轴承早期故障诊断方法,即MCKD-SK法。利用MCKD方法可以有效提取滚动轴承早期故障信号中被噪声淹没的周期冲击成分,抑制信号中的噪声,实现信号降噪,提升原信号的峭度。利用SK方法可以选择合理频带,将信号中的低频信息从高频信息中解调出来。通过仿真与实际监测数据的分析和验证,证明MCKD-SK方法可以准确有效地诊断滚动轴承的早期故障,可用于滚动轴承早期故障的在线监测。     

Faulty bearing signal recovery from large noise using a hybrid method based on spectral kurtosis and ensemble empirical mode decomposition

Time–frequency analyses are commonly used to diagnose the health of bearings by processing vibration signals captured from the bearings. However, these analyses cannot be guaranteed to be robust if the bearing signals are overwhelmed by large noise. Ensemble empirical mode decomposition (EEMD) was developed from the popular empirical mode decomposition (EMD). However, if there is large noise, it may be difficult to recover impulses from large noise. In this paper, we develop a hybrid signal processing method that combines spectral kurtosis (SK) with EEMD. First, the raw vibration signal is filtered using an optimal band-pass filter based on SK. EEMD method is then applied to decompose the filtered signal. Various bearing signals are used to validate the efficiency of the proposed method. The results demonstrate that the hybrid signal processing method can successfully recover the impulses generated by bearing faults from the raw signal, even when overwhelmed by large noise.     

小波包和小波脊线相结合的解调方法在液压泵故障诊断中的应用

针对轴向柱塞泵故障振动信号呈现出的非平稳和非线性特点,提出了一种基于小波包能量法与小波脊线法相结合的信号解调方法,将其用于液压泵故障诊断中的信号解调过程。该方法首先对原始振动信号进行功率谱分析,明确故障振动信号反映出的能量集中频带带宽;根据确定的带宽和原始信号分析频率设定小波包分解的层数,采用小波包能量法提取出分解系数对应频带能量最大的特征信息进行信号重构;利用小波脊线法对重构后的频带信号进行解调处理,通过信号的包络解调谱提取故障的特征频率,利用解调后的时频谱对液压泵单柱塞滑靴磨损、斜盘磨损以及中心弹簧故障进行分析。通过实验结果验证,该方法能有效地对液压泵的故障信号进行解调,并能找出反映故障的敏感特征频率。     

基于EMD和峭度的Hilbert包络解调在滚动轴承故障诊断中的应用分析

针对直接运用快速傅里叶变换（FFT）无法有效提取具有非线性非平稳特性的滚动轴承振动信号故障特征频率的问题,提出了一种基于经验模式分解和峭度指标的Hilbert包络解调方法.首先对滚动轴承的振动信号进行了经验模式分解（EMD）,得到了包含轴承故障特征信息的各阶本征模态函数（IMF）,再计算各阶IMF的峭度值,选取了峭度值较大的几阶IMF分量重构信号,并对重构信号进行了Hilbert包络解调分析,从而获得了滚动轴承的准确故障特征信息.分别对仿真模拟信号和实际滚动轴承发生内圈故障的振动信号进行了分析,清晰地得到了故障特征频率.研究结果表明,利用融合EMD、峭度系数和Hilbert包络解调的诊断方法能够快速、准确地提取滚动轴承的故障特征频率,从而可以对滚动轴承进行有效地故障诊断.     

基于改进经验小波变换的行星齿轮箱故障诊断

行星齿轮箱振动信号具有复杂多分量和调幅-调频的特点。幅值解调和频率解调方法能够避免传统Fourier频谱中的复杂边带分析,有效识别故障特征频率。经验小波变换通过对信号Fourier频谱的分割构造一组正交滤波器组,能提取具有紧支撑Fourier频谱的单分量成分,再对单分量成分运用Hilbert变换即可实现信号的解调分析。经验小波变换能够有效分离出调幅-调频成分,不存在模态混叠现象,具有完备的理论基础,自适应性好、算法简单、计算速度快。将改进的经验小波变换应用于行星齿轮箱振动信号的解调分析;提出了一种单分量个数的估算方法,解决了经验小波变换中的Fourier频谱划分问题;给出了对故障敏感的信号分量的选取方法,提高了分析的针对性。将改进方法应用于行星齿轮箱振动仿真信号和实验信号分析,验证了该方法的有效性。     

解调分析在机械振动分析中应用的局限性研究

在具有齿轮、滚动轴承的机械设备的振动故障诊断中 ,广泛使用解调方法分析诊断故障。从理论上分析了现有的解调分析方法存在有三个局限性 :将不包括调制信息 (故障信息 )的两时域相加信号 ,也以其频率之差作为解调信号而解出 ;广义检波滤波解调分析中 ,由于取绝对值或检波过程可能产生混频效应 ;几种细化解调分析新算法中 ,无法在细化分析的选抽时进行数字低通滤波 ,有可能会出现调制频率的高次谐波成分发生频率混叠而反折到低频部分的现象。无论那种局限性出现 ,都会在机械振动信号的解调谱中出现无法分析的频率成分 ,给分析带来很大的困难 ,有时甚至引起误诊断。     

Spectral kurtosis based on AR model for fault diagnosis and condition monitoring of rolling bearing

Spectral kurtosis (SK) is an algorithm that gives an indication of how kurtosis varies with frequency. A frequency band that contains abundant information, especially the impact signal, can be tracked by calculating SK. In the present article, SK combined with Autoregressive AR model, was applied into the fault diagnosis and condition monitoring of bearings. Accelerated life test of rolling bearings in Hangzhou Bearing Test & Research Center (HBRC) was performed to collect vibration data over their entire lifetime (normal-fault-failure). The result shows that SK can detect early incipient fault by eliminating some other interfering frequency components. In addition, it can detect fault 5 min earlier than root mean value (RMS). This fault detection in advance is significant for condition monitoring.     

Fault feature extraction of rolling bearing based on an improved cyclical spectrum density method

The traditional cyclical spectrum density(CSD) method is widely used to analyze the fault signals of rolling bearing. All modulation frequencies are demodulated in the cyclic frequency spectrum. Consequently, recognizing bearing fault type is difficult. Therefore, a new CSD method based on kurtosis(CSDK) is proposed. The kurtosis value of each cyclic frequency is used to measure the modulation capability of cyclic frequency. When the kurtosis value is large, the modulation capability is strong. Thus, the kurtosis value is regarded as the weight coefficient to accumulate all cyclic frequencies to extract fault features. Compared with the traditional method, CSDK can reduce the interference of harmonic frequency in fault frequency, which makes fault characteristics distinct from background noise. To validate the effectiveness of the method, experiments are performed on the simulation signal, the fault signal of the bearing outer race in the test bed, and the signal gathered from the bearing of the blast furnace belt cylinder. Experimental results show that the CSDK is better than the resonance demodulation method and the CSD in extracting fault features and recognizing degradation trends. The proposed method provides a new solution to fault diagnosis in bearings.     

基于倒谱预白化和随机共振的轴承故障增强检测

轴承损伤引起的冲击受到离散频率分量和噪声干扰,使轴承故障检测面临困难。结合基于倒谱编辑(Cepstrum editing procedure, CEP)的信号预白化和随机共振(Stochastic resonance, SR)微弱信号检测技术,提出一种轴承故障增强检测的新方法。信号预白化能够提升轴承振动信号的冲击特性,产生包含白噪声和轴承局部故障信号的白化信号。在未知最优共振频带的情况下,对白化后的轴承振动信号进行包络分析,增强故障特征分量的同时引入了较多噪声。通过随机共振的归一化尺度变换,将轴承包络信号作为检测模型的输入,增强轴承故障特征频率分量。提出将轴承故障特征频率处的局部谱峭度和局部信噪比作为对照指标。实测正常和外环植入故障轴承的诊断结果表明,提出的方法优于基于谱峭度优化的包络分析和单纯的信号预白化方法。